國立虎尾科技大學 |

Network Data Analytics = A Hands-On Approach for Application Development /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Network Data Analytics/ by K. G. Srinivasa, Siddesh G. M., Srinidhi H.
Reminder of title:	A Hands-On Approach for Application Development /
Author:	Srinivasa, K. G.
other author:	G. M., Siddesh.
Description:	XXV, 398 p. 155 illus., 117 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Data mining. -
Online resource:	https://doi.org/10.1007/978-3-319-77800-6
ISBN:	9783319778006

Network Data Analytics = A Hands-On Approach for Application Development /
Srinivasa, K. G.

Network Data AnalyticsA Hands-On Approach for Application Development /[electronic resource] :by K. G. Srinivasa, Siddesh G. M., Srinidhi H. - 1st ed. 2018. - XXV, 398 p. 155 illus., 117 illus. in color.online resource. - Computer Communications and Networks,1617-7975. - Computer Communications and Networks,.

Part I: Data Analytics and Hadoop -- Chapter 1. Introduction to Data Analytics -- Chapter 2. Introduction to Hadoop -- Chapter 3. Data Analytics with Map Reduce -- Part II: Tools for Data Analytics -- Chapter 4. Apache Pig -- Chapter 5. Apache Hive -- Chapter 6. Apache Spark -- Chapter 7. Apache Flume -- Chapter 8. Apache Storm -- Chapter 9. Python R -- Part III: Machine Learning for Data Analytics -- Chapter 10. Basics of Machine Learning -- Chapter 11. Linear Regression -- Chapter 12. Logistic Regression -- Chapter 13. Machine Learning on Spark -- Part IV: Exploring and Visualizing Data -- Chapter 14. Introduction to Visualization -- Chapter 15. Principles of Data Visualization -- Chapter 16. Visualization Charts -- Chapter 17. Popular Visualization Tools -- Chapter 18. Data Visualization with Hadoop -- Part V: Case Studies -- Chapter 19. Product Recommendation -- Chapter 20. Market Basket Analysis.

In order to carry out data analytics, we need powerful and flexible computing software. However the software available for data analytics is often proprietary and can be expensive. This book reviews Apache tools, which are open source and easy to use. After providing an overview of the background of data analytics, covering the different types of analysis and the basics of using Hadoop as a tool, it focuses on different Hadoop ecosystem tools, like Apache Flume, Apache Spark, Apache Storm, Apache Hive, R, and Python, which can be used for different types of analysis. It then examines the different machine learning techniques that are useful for data analytics, and how to visualize data with different graphs and charts. Presenting data analytics from a practice-oriented viewpoint, the book discusses useful tools and approaches for data analytics, supported by concrete code examples. The book is a valuable reference resource for graduate students and professionals in related fields, and is also of interest to general readers with an understanding of data analytics.

ISBN: 9783319778006

Standard No.: 10.1007/978-3-319-77800-6doiSubjects--Topical Terms:

528622
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Network Data Analytics = A Hands-On Approach for Application Development /
LDR:03481nam a22004215i 4500 001 992880
003 DE-He213
005 20200703153818.0
007 cr nn 008mamaa
008 201225s2018 gw | s |||| 0|eng d
020 $a 9783319778006 $9 978-3-319-77800-6
024 7 $a 10.1007/978-3-319-77800-6 $2 doi
035 $a 978-3-319-77800-6
050 4 $a QA76.9.D343
072 7 $a UNF $2 bicssc
072 7 $a COM021030 $2 bisacsh
072 7 $a UNF $2 thema
072 7 $a UYQE $2 thema
082 0 4 $a 006.312 $2 23
100 1 $a Srinivasa, K. G. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 897249
245 1 0 $a Network Data Analytics $h [electronic resource] : $b A Hands-On Approach for Application Development / $c by K. G. Srinivasa, Siddesh G. M., Srinidhi H.
250 $a 1st ed. 2018.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a XXV, 398 p. 155 illus., 117 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Computer Communications and Networks, $x 1617-7975
505 0 $a Part I: Data Analytics and Hadoop -- Chapter 1. Introduction to Data Analytics -- Chapter 2. Introduction to Hadoop -- Chapter 3. Data Analytics with Map Reduce -- Part II: Tools for Data Analytics -- Chapter 4. Apache Pig -- Chapter 5. Apache Hive -- Chapter 6. Apache Spark -- Chapter 7. Apache Flume -- Chapter 8. Apache Storm -- Chapter 9. Python R -- Part III: Machine Learning for Data Analytics -- Chapter 10. Basics of Machine Learning -- Chapter 11. Linear Regression -- Chapter 12. Logistic Regression -- Chapter 13. Machine Learning on Spark -- Part IV: Exploring and Visualizing Data -- Chapter 14. Introduction to Visualization -- Chapter 15. Principles of Data Visualization -- Chapter 16. Visualization Charts -- Chapter 17. Popular Visualization Tools -- Chapter 18. Data Visualization with Hadoop -- Part V: Case Studies -- Chapter 19. Product Recommendation -- Chapter 20. Market Basket Analysis.
520 $a In order to carry out data analytics, we need powerful and flexible computing software. However the software available for data analytics is often proprietary and can be expensive. This book reviews Apache tools, which are open source and easy to use. After providing an overview of the background of data analytics, covering the different types of analysis and the basics of using Hadoop as a tool, it focuses on different Hadoop ecosystem tools, like Apache Flume, Apache Spark, Apache Storm, Apache Hive, R, and Python, which can be used for different types of analysis. It then examines the different machine learning techniques that are useful for data analytics, and how to visualize data with different graphs and charts. Presenting data analytics from a practice-oriented viewpoint, the book discusses useful tools and approaches for data analytics, supported by concrete code examples. The book is a valuable reference resource for graduate students and professionals in related fields, and is also of interest to general readers with an understanding of data analytics.
650 0 $a Data mining. $3 528622
650 0 $a Big data. $3 981821
650 0 $a Mathematics. $3 527692
650 0 $a Visualization. $3 574210
650 0 $a Artificial intelligence. $3 559380
650 1 4 $a Data Mining and Knowledge Discovery. $3 677765
650 2 4 $a Big Data. $3 1017136
650 2 4 $a Artificial Intelligence. $3 646849
700 1 $a G. M., Siddesh. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1203517
700 1 $a H., Srinidhi. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1203518
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319777993
776 0 8 $i Printed edition: $z 9783319778013
776 0 8 $i Printed edition: $z 9783030085445
830 0 $a Computer Communications and Networks, $x 1617-7975 $3 1255420
856 4 0 $u https://doi.org/10.1007/978-3-319-77800-6
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)